Energy Efficient Level by Level Scheduling for Multiple Workflows in Cloud

Abstract

Cloud computing provides paradigms for hosting a large number of services by providing configurable computing resources on demand, causing multiple applications to be hosted simultaneously in the form of multiple workflows which requires large scale data centers. The reduction in energy consumption is one of the major concerns of large-scale data centers. In this article, the authors develop a multiple workflow scheduling heuristic with the aim to reduce energy consumption along with the execution time. In the proposed approach, first tasks are considered level by level following the precedence constraints as a result of the tasks multiple DAGs could run in parallel. The tasks at each level are considered in the order of their ranking and scheduled on the efficient processor based on their estimated finish time where idle slots of resources are used to reduce the overall makespan and energy consumption. Finally, DVFS is applied during idle slots and the communication phase to reduce energy consumption further by scaling the frequency to appropriate level.